Prediction of amphiphilic cell-penetrating peptide building blocks from protein-derived amino acid sequences for engineering of drug delivery nanoassemblies

0534088 - FZÚ 2021 RIV US eng J - Journal Article
Feger, G. - Angelov, Borislav - Angelova, A.
Prediction of amphiphilic cell-penetrating peptide building blocks from protein-derived amino acid sequences for engineering of drug delivery nanoassemblies.
Journal of Physical Chemistry B. Roč. 124, č. 20 (2020), s. 4069-4078. ISSN 1520-6106. E-ISSN 1520-5207
R&D Projects: GA MŠMT EF16_019/0000789; GA MŠMT EF15_003/0000447
Grant - others:OP VVV - ADONIS(XE) CZ.02.1.01/0.0/0.0/16_019/0000789; OP VVV - ELIBIO(XE) CZ.02.1.01/0.0/0.0/15_003/0000447
Institutional support: RVO:68378271
Keywords : small-angle scattering * structural-characterization * bioactive peptides * rational design * active peptides * helical peptide * surfactant * nanotubes
OECD category: Biophysics
Impact factor: 2.991, year: 2020
Method of publishing: Limited access
https://doi.org/10.1021/acs.jpcb.0c01618

Amphiphilic molecules, forming self-assembled nanoarchitectures, are typically composed of hydrophobic and hydrophilic domains. Peptide amphiphiles can be designed from two, three, or four building blocks imparting novel structural and functional properties and affinities for interaction with cellular membranes or intracellular organelles. Here we present a combined numerical approach to design amphiphilic peptide scaffolds that are derived from the human nuclear K-i-67 protein. K-i-67 acts, like a biosurfactant, as a steric and electrostatic charge barrier against the collapse of mitotic chromosomes. The proposed predictive design of new K-i-67 protein-derived amphiphilic amino acid sequences exploits the computational outcomes of a set of web-accessible predictors, which are based on machine learning methods.

Permanent Link: http://hdl.handle.net/11104/0312306